Some unsupervised learning approaches [1]}, [2]} were proposed to address this problem. They usually use image reconstruction loss and consistency loss without the need for ground truth depth data.
They can also mitigate domain gaps by training directly with real-world stereo images without corresponding registered depth maps.
